Segmenting Consumers of Bath Soap
library(tidyverse)
library(factoextra)
library(ISLR)
library(GGally)
library(viridis)
library(flexclust)
library(dplyr)
library(ggplot2)
setwd("~/Desktop")
BathSoap <- read.csv("BathSoap.csv")
BathSoap <- data.frame(lapply(BathSoap, function(x) as.numeric(sub("%", "", x))))
BathSoap <- BathSoap %>% mutate_at(c(20:46), funs(./100)) %>% mutate_at(c(20:46), funs(.*BathSoap$Total.Volume))
head(BathSoap)
## Member.id SEC FEH MT SEX AGE EDU HS CHILD CS Affluence.Index
## 1 1010010 4 3 10 1 4 4 2 4 1 2
## 2 1010020 3 2 10 2 2 4 4 2 1 19
## 3 1014020 2 3 10 2 4 5 6 4 1 23
## 4 1014030 4 0 0 0 4 0 0 5 0 0
## 5 1014190 4 1 10 2 3 4 4 3 1 10
## 6 1017020 4 3 10 2 3 4 5 2 1 13
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 3 17 8025 24 818.0
## 2 5 25 13975 40 1681.5
## 3 5 37 23100 63 1950.0
## 4 2 4 1500 4 114.0
## 5 3 6 8300 13 591.0
## 6 3 26 18175 41 1705.5
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 1.41 334.38 10.19 8025.00
## 2 1.60 349.38 12.03 12437.75
## 3 1.70 366.67 8.44 21714.00
## 4 1.00 375.00 7.60 1500.00
## 5 2.17 638.46 7.12 5063.00
## 6 1.58 443.29 9.38 18175.00
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 0.0 0.0 3049.5 1043.25
## 2 1397.5 279.5 279.5 1118.00
## 3 462.0 924.0 693.0 12705.00
## 4 0.0 0.0 600.0 900.00
## 5 1162.0 1992.0 415.0 1162.00
## 6 0.0 0.0 1454.0 1272.25
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 0 0 0 0.0 0 0.0
## 2 0 0 0 838.5 0 1956.5
## 3 0 693 0 0.0 0 462.0
## 4 0 0 0 0.0 0 0.0
## 5 0 0 0 0.0 0 0.0
## 6 0 0 0 0.0 0 0.0
## Others.999 Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5 PropCat.6
## 1 3948.300 1845.75 4494.00 1043.25 561.75 4012.50 0.00
## 2 9768.525 4052.75 7686.25 1257.75 838.50 6428.50 4891.25
## 3 8754.900 2772.00 7392.00 12936.00 0.00 5544.00 2772.00
## 4 0.000 0.00 600.00 900.00 0.00 600.00 0.00
## 5 6698.100 0.00 415.00 1162.00 6723.00 6723.00 0.00
## 6 15575.975 3998.50 8178.75 1272.25 4907.25 8905.75 1817.50
## PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11 PropCat.12
## 1 0.00 0.00 0.00 0 0.0 240.75
## 2 419.25 279.50 139.75 0 838.5 0.00
## 3 693.00 231.00 231.00 0 0.0 462.00
## 4 0.00 0.00 0.00 0 0.0 0.00
## 5 0.00 415.00 0.00 0 0.0 0.00
## 6 0.00 181.75 1272.25 0 0.0 0.00
## PropCat.13 PropCat.14 PropCat.15
## 1 0 1043.25 2728.50
## 2 0 1118.00 0.00
## 3 0 12936.00 0.00
## 4 0 900.00 0.00
## 5 0 1162.00 0.00
## 6 0 1272.25 4907.25
BS <- scale(BathSoap[,-c(1:11)])
BS <- cbind(BathSoap[,1:11],BS)
head(BS)
## Member.id SEC FEH MT SEX AGE EDU HS CHILD CS Affluence.Index
## 1 1010010 4 3 10 1 4 4 2 4 1 2
## 2 1010020 3 2 10 2 2 4 4 2 1 19
## 3 1014020 2 3 10 2 4 5 6 4 1 23
## 4 1014030 4 0 0 0 4 0 0 5 0 0
## 5 1014190 4 1 10 2 3 4 4 3 1 10
## 6 1017020 4 3 10 2 3 4 5 2 1 13
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 -0.4030277 0.1200727 -0.5005898 -0.4104681 -0.5881031
## 2 0.8630280 0.8895639 0.2651391 0.5076339 0.3896410
## 3 0.8630280 2.0438006 1.4394712 1.8274054 0.6936645
## 4 -1.0360556 -1.1303505 -1.3403176 -1.5580955 -1.3852447
## 5 -0.4030277 -0.9379777 -0.4651989 -1.0416632 -0.8451360
## 6 -0.4030277 0.9857502 0.8056536 0.5650152 0.4168163
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 -0.4636969 -0.3242918 -0.43944366 -0.3943558
## 2 -0.3907514 -0.2639930 0.05217678 0.1983882
## 3 -0.3523590 -0.1944886 -0.90701745 1.4444233
## 4 -0.6211057 -0.1610026 -1.13145287 -1.2708284
## 5 -0.1719147 0.8980852 -1.25970168 -0.7922274
## 6 -0.3984298 0.1135176 -0.65586353 0.9690461
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 -0.5574329 -0.5116939 0.2226321 -0.1754393
## 2 0.7686897 -0.1252184 -0.4805832 -0.1578175
## 3 -0.1190296 0.7659568 -0.3756086 2.5737501
## 4 -0.5574329 -0.5116939 -0.3992184 -0.2092097
## 5 0.5452179 2.2427218 -0.4461840 -0.1474447
## 6 -0.5574329 -0.5116939 -0.1824148 -0.1214539
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## 2 -0.3448178 -0.2253619 -0.2371511 0.4047639 -0.2549821 2.6176765
## 3 -0.3448178 0.1428499 -0.2371511 -0.2538919 -0.2549821 0.3943628
## 4 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## 5 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## 6 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## Others.999 Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5
## 1 -0.3870628 -0.2794416143 -0.2520866 -0.1988988 -0.1925159 -0.246319991
## 2 0.6565013 0.3854838369 0.2625352 -0.1496659 -0.1056810 0.141979507
## 3 0.4747587 -0.0003808082 0.2150992 2.5307700 -0.3687742 -0.000177325
## 4 -1.0949914 -0.8355295851 -0.8798374 -0.2317780 -0.3687742 -0.794776958
## 5 0.1059753 -0.8355295851 -0.9096612 -0.1716428 1.7406778 0.189311544
## 6 1.6977747 0.3691393848 0.3419310 -0.1463378 1.1709563 0.540123103
## PropCat.6 PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11
## 1 -0.4980207 -0.4173594 -0.5069920 -0.4317488 -0.2850019 -0.2651424
## 2 1.6247065 -0.2567103 -0.3267324 -0.2787491 -0.2850019 0.3729320
## 3 0.7049846 -0.1518142 -0.3580118 -0.1788477 -0.2850019 -0.2651424
## 4 -0.4980207 -0.4173594 -0.5069920 -0.4317488 -0.2850019 -0.2651424
## 5 -0.4980207 -0.4173594 -0.2393435 -0.4317488 -0.2850019 -0.2651424
## 6 0.2907463 -0.4173594 -0.3897749 0.9611232 -0.2850019 -0.2651424
## PropCat.12 PropCat.13 PropCat.14 PropCat.15
## 1 0.8905060 -0.2536688 -0.1912455 2.1061267
## 2 -0.2907978 -0.2536688 -0.1739337 -0.2505867
## 3 1.9761280 -0.2536688 2.5630697 -0.2505867
## 4 -0.2907978 -0.2536688 -0.2244217 -0.2505867
## 5 -0.2907978 -0.2536688 -0.1637435 -0.2505867
## 6 -0.2907978 -0.2536688 -0.1382100 3.9879993
Identify clusters of households based on Purchase behavior
fviz_nbclust(BS[,c(12:31)], kmeans, method = "wss")
fviz_nbclust(BS[,c(12:31)], kmeans, method = "silhouette")
k = 2
set.seed(120)
pk2 <- kmeans(BS[,c(12:31)], centers = 2, nstart = 25)
pk2
## K-means clustering with 2 clusters of sizes 148, 452
##
## Cluster means:
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 0.3839799 0.5256153 1.3353247 0.7871742 1.176034
## 2 -0.1257279 -0.1721041 -0.4372302 -0.2577473 -0.385073
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 0.18290360 0.7673642 -0.27557620 1.2822270
## 2 -0.05988879 -0.2512609 0.09023291 -0.4198442
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 0.4821548 0.4661500 0.3593261 0.4911850
## 2 -0.1578737 -0.1526332 -0.1176554 -0.1608305
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 0.16526060 0.20168930 0.13563777 0.30345639 0.08320394 0.24331419
## 2 -0.05411188 -0.06603986 -0.04441237 -0.09936183 -0.02724377 -0.07966925
## Others.999
## 1 0.9988818
## 2 -0.3270675
##
## Clustering vector:
## [1] 2 1 1 2 2 1 2 2 1 2 1 2 1 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 1
## [36] 2 2 2 2 2 2 2 1 2 1 2 2 2 2 1 1 2 2 1 2 1 1 1 1 2 1 1 1 2 2 2 1 1 2 2
## [71] 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 1 2 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 1 2
## [106] 1 1 2 2 2 2 2 2 1 1 2 1 2 1 2 1 1 2 1 1 1 1 2 2 1 1 1 2 2 2 2 2 2 2 1
## [141] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 1 2 1 2 2 2 2 2 1
## [176] 2 1 2 1 2 2 1 2 2 1 2 1 1 1 2 2 1 2 2 1 2 2 2 1 2 2 1 1 2 2 2 2 1 2 1
## [211] 2 2 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 1 2 1 2 1 1 2 1 2 2 2 2 2 2
## [246] 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 1 1 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2
## [281] 1 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1
## [316] 1 2 2 1 2 1 2 1 1 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 2 2 2
## [351] 2 2 2 2 2 2 1 2 2 1 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1
## [386] 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2
## [421] 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [456] 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2
## [491] 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2
## [526] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2
## [561] 2 2 2 2 1 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2
## [596] 2 2 1 2 2
##
## Within cluster sum of squares by cluster:
## [1] 5379.942 4907.679
## (between_SS / total_SS = 14.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
fviz_cluster(pk2, data = BS)
Identify clusters of households based on Basis of purchase
fviz_nbclust(BS[,c(32:46)], kmeans, method = "wss")
fviz_nbclust(BS[,c(32:46)], kmeans, method = "silhouette")
k = 4
set.seed(120)
bk4 <- kmeans(BS[,c(32:46)], centers = 4, nstart = 25)
bk4
## K-means clustering with 4 clusters of sizes 99, 48, 56, 397
##
## Cluster means:
## Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5 PropCat.6
## 1 0.1661731 1.1324062 -0.1806369 0.77916278 1.4390664 -0.01285879
## 2 1.8559024 0.1231322 -0.3602898 -0.24398568 -0.2408319 0.97459347
## 3 -0.5683449 -0.5400881 2.6903448 -0.08367702 -0.5188230 0.07317085
## 4 -0.1856603 -0.2210923 -0.2908875 -0.15299720 -0.2565581 -0.12494971
## PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11 PropCat.12
## 1 -0.09915360 0.07733577 0.93989828 -0.09869787 0.3519956 -0.1030397
## 2 1.15236127 0.02499432 0.05503680 1.09157261 -0.0161106 1.8006345
## 3 -0.34781908 -0.40801906 -0.05306282 -0.25297241 -0.1928943 -0.1256151
## 4 -0.06553971 0.03524710 -0.23355209 -0.07168247 -0.0586201 -0.1742949
## PropCat.13 PropCat.14 PropCat.15
## 1 -0.2171882 -0.1806510 0.19702711
## 2 1.5052363 -0.3567717 -0.02484209
## 3 -0.1748551 2.6852954 -0.15972750
## 4 -0.1031683 -0.2905971 -0.02359830
##
## Clustering vector:
## [1] 4 4 3 4 4 1 4 3 1 4 1 4 1 4 4 4 1 4 1 4 4 3 3 4 4 2 4 1 4 4 3 4 4 3 3
## [36] 4 4 4 3 4 4 3 3 4 1 4 4 4 4 2 3 1 4 1 3 1 3 1 3 4 4 3 3 4 4 3 3 1 4 4
## [71] 4 4 3 4 4 4 4 4 2 4 4 3 3 4 4 1 4 1 4 3 2 3 3 4 2 4 3 4 4 4 4 4 1 4 1
## [106] 1 2 4 4 4 3 1 4 1 1 4 4 3 1 4 1 2 1 2 1 3 1 4 4 2 1 2 4 4 4 2 4 4 4 1
## [141] 1 3 4 3 2 3 4 4 4 4 4 4 4 3 4 4 4 4 4 3 3 3 1 2 1 4 1 4 1 4 1 4 4 3 1
## [176] 4 1 3 3 1 4 1 2 4 1 1 1 1 1 4 1 2 4 4 4 4 4 4 2 4 1 1 3 4 3 4 4 1 4 1
## [211] 4 4 4 1 1 4 4 4 3 2 4 1 3 4 4 4 4 4 4 4 3 1 3 2 4 3 3 3 3 4 4 4 4 4 3
## [246] 4 4 4 4 4 4 4 4 1 1 4 1 3 1 4 4 4 4 1 1 4 4 4 4 4 1 1 4 4 4 4 1 4 4 4
## [281] 1 1 4 1 1 4 4 4 4 2 4 4 4 4 4 1 3 1 1 2 4 4 4 2 4 4 4 4 4 2 4 1 1 2 1
## [316] 1 4 4 4 4 3 4 1 3 4 4 2 4 4 4 1 4 4 4 4 4 1 4 4 4 4 4 1 4 4 4 4 4 4 4
## [351] 4 4 2 4 4 4 2 4 2 4 4 4 1 2 2 4 4 4 4 4 4 4 4 4 3 4 4 4 1 4 4 4 1 4 4
## [386] 4 4 1 4 4 2 4 4 4 4 4 4 4 4 4 4 2 1 2 4 4 4 4 2 4 4 4 2 4 4 4 4 4 4 4
## [421] 1 4 4 4 4 4 4 4 4 4 1 1 1 4 2 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 2 4
## [456] 4 1 2 1 4 4 4 4 2 1 3 4 4 4 4 4 4 4 1 4 1 1 4 4 2 4 4 4 2 4 4 4 4 4 4
## [491] 4 4 4 4 2 2 4 4 1 4 3 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 1 4
## [526] 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4 1 4 4 4 4 4 4 4 4 4 4 1 4 4 2
## [561] 4 1 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 2 4 4 1 4 4 4 4 4
## [596] 4 4 1 4 4
##
## Within cluster sum of squares by cluster:
## [1] 2252.4953 1891.6807 410.5978 2231.0908
## (between_SS / total_SS = 24.5 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
fviz_cluster(bk4, data = BS)
Purchase behavior and Basis of purchase, k = 3
fviz_nbclust(BS[,c(12:46)], kmeans, method = "wss")
fviz_nbclust(BS[,c(12:46)], kmeans, method = "silhouette")
set.seed(120)
k3 <- kmeans(BS[,c(12:46)], centers = 3, nstart = 25)
k3
## K-means clustering with 3 clusters of sizes 424, 54, 122
##
## Cluster means:
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 -0.09248577 -0.1321898 -0.4656020 -0.25848864 -0.3708717
## 2 -0.35613679 -0.5924191 0.7172361 -0.01198633 -0.1557435
## 3 0.47906028 0.7216319 1.3006927 0.90365940 1.3578667
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 -0.15865849 -0.2872994 0.18397336 -0.4467837
## 2 1.35361406 0.7513708 -1.35875806 0.6967571
## 3 -0.04773737 0.6659092 -0.03796531 1.2443557
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 -0.14768724 -0.1952011 -0.09251371 -0.2952935
## 2 -0.05519063 0.6157772 -0.44555219 2.7091279
## 3 0.53770232 0.4058468 0.51873469 -0.1728562
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 -0.03609944 -0.05447222 -0.03827278 -0.0913534 -0.01319092 -0.05926465
## 2 -0.27633726 -0.17052912 -0.13783678 -0.1765512 -0.21847185 -0.17453774
## 3 0.24777356 0.26479340 0.19402330 0.3956361 0.14254450 0.28322337
## Others.999 Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5
## 1 -0.2982449 -0.1419520 -0.2328618 -0.2977887 -0.1273257 -0.2135347
## 2 -0.4971270 -0.5788665 -0.6174472 2.7013045 -0.1323849 -0.5807933
## 3 1.2565630 0.7495611 1.0825866 -0.1607215 0.5011054 0.9991930
## PropCat.6 PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11
## 1 -0.1500091 -0.06716428 -0.05191804 -0.15715382 -0.01087804 -0.09104144
## 2 -0.0746225 -0.35998537 -0.41496499 -0.05220554 -0.25178613 -0.19021844
## 3 0.5543727 0.39276120 0.36410949 0.56928130 0.14925196 0.40060137
## PropCat.12 PropCat.13 PropCat.14 PropCat.15
## 1 -0.1015529 -0.03311153 -0.2972731 -0.07129654
## 2 -0.1194972 -0.17193607 2.7170638 -0.15636234
## 3 0.4058302 0.19117899 -0.1694891 0.31699425
##
## Clustering vector:
## [1] 1 1 2 1 1 3 1 2 3 1 3 1 3 1 1 1 3 1 1 1 2 2 2 1 1 3 1 1 1 1 2 1 1 2 2
## [36] 1 1 1 2 1 1 2 2 1 3 1 1 1 1 3 2 1 1 3 2 3 2 3 2 1 3 2 2 1 1 2 2 3 1 1
## [71] 1 1 2 1 1 1 1 1 3 1 1 2 2 1 1 3 1 3 1 2 1 2 2 1 1 1 2 1 1 1 1 1 3 3 1
## [106] 3 3 1 1 1 2 1 1 3 3 1 3 2 3 1 3 3 1 3 3 2 3 1 1 3 3 3 1 1 2 1 1 1 1 3
## [141] 1 2 1 1 3 2 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 2 3 3 3 1 3 1 3 1 1 1 1 2 3
## [176] 1 3 2 2 1 1 3 1 1 3 1 3 3 3 1 1 3 1 1 1 1 1 1 3 1 1 3 3 1 2 1 1 3 1 3
## [211] 1 1 1 3 3 1 1 1 2 1 1 3 2 1 1 1 1 3 1 1 2 3 2 3 1 2 2 2 2 1 1 1 1 1 2
## [246] 1 1 1 1 1 1 1 1 3 1 1 1 2 3 1 1 1 1 3 3 1 1 1 1 1 3 3 1 1 1 1 3 1 1 1
## [281] 3 3 1 3 3 1 1 1 1 1 1 1 1 1 1 1 2 1 3 1 1 1 1 3 1 1 1 1 1 1 1 3 3 1 3
## [316] 3 1 1 3 1 2 1 3 3 1 1 3 1 1 1 3 1 1 1 1 1 3 1 1 1 1 1 3 1 1 1 3 1 1 1
## [351] 1 1 1 1 1 1 3 1 1 3 1 1 3 3 3 1 1 1 1 1 1 1 1 1 2 1 1 1 3 1 1 1 3 1 3
## [386] 1 1 1 1 1 3 1 1 1 3 1 1 1 1 1 1 3 3 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1
## [421] 3 1 1 1 1 3 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [456] 1 1 3 3 1 1 1 1 1 3 2 1 1 1 1 1 1 1 1 3 3 1 1 1 3 1 1 1 1 1 1 1 1 1 1
## [491] 1 1 1 1 3 1 1 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 3 1
## [526] 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 3 3 3 1 1 1 3 1 1 1 3 1 1 1 1 1 1
## [561] 1 1 1 1 3 1 1 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 1 1 3 1 1 3 1 1 1 1 1
## [596] 1 1 3 1 1
##
## Within cluster sum of squares by cluster:
## [1] 7224.622 1215.646 8528.354
## (between_SS / total_SS = 19.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
fviz_cluster(k3, data = BS)
k3center <- as.data.frame(k3$centers)
colnames(k3center) <- c("No.Br","Br.Runs","Total.Vol","No.Trans","Value","Trans.Br.Runs","Vol.Tran","Avg.Price","PVNP","PVP6","PVOP","Br.57.144","Br.55","Br.272","Br.286","Br.24","Br.481","Br.352","Br.5","Br.999","Pt1","Pt2","Pt3","Pt4","Pt5","Pt6","Pt7","Pt8","Pt9","Pt10","Pt11","Pt12","Pt13","Pt14","Pt15")
cluster <- matrix(c("1","2","3"),nrow = 3)
k3center <- cbind(cluster,k3center)
Purchase summary over the period, k = 3
ggparcoord(k3center, columns = 2:9, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Brandwise purchase, k = 3
ggparcoord(k3center, columns = 13:21, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Price categorywise purchase, k = 3
ggparcoord(k3center, columns = 22:25, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Selling propositionwise purchase, k = 3
ggparcoord(k3center, columns = 26:36, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Purchase behavior and Basis of purchase, k = 2
set.seed(120)
k2 <- kmeans(BS[,c(12:46)], centers = 2, nstart = 25)
k2
## K-means clustering with 2 clusters of sizes 466, 134
##
## Cluster means:
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 -0.1449263 -0.2041952 -0.3847868 -0.2643057 -0.3750085
## 2 0.5039973 0.7101116 1.3381391 0.9191525 1.3041341
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 -0.001571150 -0.1865315 0.03398412 -0.3661046
## 2 0.005463849 0.6486840 -0.11818358 1.2731696
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 -0.1679486 -0.1283127 -0.1297062 -0.03331724
## 2 0.5840601 0.4462216 0.4510677 0.11586443
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 -0.06196977 -0.06752008 -0.05093196 -0.1011599 -0.0336199 -0.07533559
## 2 0.21550681 0.23480864 0.17712160 0.3517949 0.1169170 0.26198795
## Others.999 Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5
## 1 -0.3379028 -0.1938666 -0.2885875 -0.03667022 -0.1239964 -0.2579915
## 2 1.1750949 0.6741928 1.0035952 0.12752481 0.4312115 0.8971943
## PropCat.6 PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11
## 1 -0.1622671 -0.0955427 -0.09287433 -0.1610016 -0.03283336 -0.1020414
## 2 0.5643019 0.3322604 0.32298088 0.5599011 0.11418168 0.3548603
## PropCat.12 PropCat.13 PropCat.14 PropCat.15
## 1 -0.1073432 -0.04865676 -0.03397851 -0.08876794
## 2 0.3732981 0.16920932 0.11816407 0.30870044
##
## Clustering vector:
## [1] 1 2 2 1 1 2 1 1 2 1 2 1 2 1 1 1 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 2 2 2 1 1 2 1 1 1 1 1 2 2 1 1
## [71] 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 1
## [106] 2 2 1 1 1 1 1 1 2 2 1 2 1 2 1 2 2 1 2 2 2 2 1 1 2 2 2 1 1 1 1 1 1 1 2
## [141] 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 2 1 2 1 2 1 1 1 1 1 2
## [176] 1 2 1 2 1 1 2 1 1 2 1 2 2 2 1 1 2 1 1 1 1 1 1 2 1 1 2 2 1 1 1 1 2 1 2
## [211] 1 1 2 2 2 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1
## [246] 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1
## [281] 2 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 2 2 1 2
## [316] 2 1 1 2 1 2 1 2 2 1 1 2 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1
## [351] 1 1 1 1 1 1 2 1 1 2 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 2
## [386] 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## [421] 2 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [456] 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## [491] 1 1 1 1 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1
## [526] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1
## [561] 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1
## [596] 1 1 2 1 1
##
## Within cluster sum of squares by cluster:
## [1] 9329.794 9330.351
## (between_SS / total_SS = 11.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
fviz_cluster(k2, data = BS)
k2center <- as.data.frame(k2$centers)
colnames(k2center) <- c("No.Br","Br.Runs","Total.Vol","No.Trans","Value","Trans.Br.Runs","Vol.Tran","Avg.Price","PVNP","PVP6","PVOP","Br.57.144","Br.55","Br.272","Br.286","Br.24","Br.481","Br.352","Br.5","Br.999","Pt1","Pt2","Pt3","Pt4","Pt5","Pt6","Pt7","Pt8","Pt9","Pt10","Pt11","Pt12","Pt13","Pt14","Pt15")
cluster <- matrix(c("1","2"),nrow = 2)
k2center <- cbind(cluster,k2center)
Purchase summary over the period, k = 2
ggparcoord(k2center, columns = 2:9, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Brandwise purchase, k = 2
ggparcoord(k2center, columns = 13:21, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Price categorywise purchase, k = 2
ggparcoord(k2center, columns = 22:25, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Selling propositionwise purchase, k = 2
ggparcoord(k2center, columns = 26:36, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Purchase behavior and Basis of purchase, k = 4
set.seed(120)
k4 <- kmeans(BS[,c(12:46)], centers = 4, nstart = 25)
k4
## K-means clustering with 4 clusters of sizes 52, 159, 351, 38
##
## Cluster means:
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 -0.3178125 -0.5550818 0.8179290 0.06954917 -0.08493788
## 2 0.7435888 0.9754662 0.3226669 0.96632396 0.65925598
## 3 -0.2713724 -0.3531863 -0.5144466 -0.46539726 -0.50279058
## 4 -0.1698070 -0.0596440 2.2824841 0.16032557 2.00196217
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 1.35462582 0.7507119 -1.338110210 0.8055906
## 2 -0.20919640 -0.3438857 0.514423676 0.2621991
## 3 -0.11532150 -0.1972378 0.008589846 -0.4838254
## 4 0.08682978 2.2334547 -0.400701833 2.2695356
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 -0.03587362 0.5481555 -0.42588824 2.78575343
## 2 0.38892339 0.2020246 -0.11126248 -0.30328607
## 3 -0.21838677 -0.2361182 -0.06576176 -0.27053370
## 4 0.43895702 0.5855657 1.65577105 -0.04419375
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 -0.27370339 -0.16842017 -0.1381482 -0.17357659 -0.21706761 -0.1699839
## 2 0.22597542 0.01829106 0.4568449 0.03579013 0.01987912 0.4253193
## 3 -0.08119801 -0.04780401 -0.1648062 -0.08663000 0.02897091 -0.1531229
## 4 0.17902592 0.59549419 -0.2002015 0.88796007 -0.05373823 -0.1326441
## Others.999 Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5
## 1 -0.4318378 -0.5442678 -0.5914525 2.77808631 -0.07670589 -0.53360466
## 2 0.5919819 0.9322732 0.1553378 -0.30349931 -0.07144406 -0.04596878
## 3 -0.3849787 -0.3535908 -0.2198827 -0.27093927 -0.08855764 -0.18461162
## 4 1.6699460 0.1100221 2.1904122 -0.02906355 1.22189589 2.62776730
## PropCat.6 PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11
## 1 -0.05833796 -0.35777868 -0.4114255 0.05546797 -0.25050860 -0.18733674
## 2 0.46501829 0.37789106 0.4044906 0.35105963 0.34084122 0.04807838
## 3 -0.24130847 -0.09717295 -0.1398660 -0.18987208 -0.11606667 -0.08507712
## 4 0.36302474 -0.19401267 0.1624500 0.20900748 -0.01126065 0.84102937
## PropCat.12 PropCat.13 PropCat.14 PropCat.15
## 1 -0.1129087 -0.1721924 2.79384348 -0.1527383
## 2 0.3570557 0.4759960 -0.30278794 0.3052338
## 3 -0.1672040 -0.1707809 -0.27083693 -0.1020832
## 4 0.2049469 -0.1785600 -0.05454774 -0.1252252
##
## Clustering vector:
## [1] 3 2 1 3 3 2 3 1 4 3 2 2 4 3 3 3 2 3 3 3 3 1 1 3 3 2 3 3 3 3 1 3 3 1 1
## [36] 3 3 3 1 3 3 1 1 3 4 3 3 3 3 2 1 3 3 4 1 4 1 2 1 3 2 1 1 3 2 1 1 2 3 3
## [71] 3 3 1 3 3 3 3 3 2 3 3 1 1 3 3 3 3 4 3 1 2 1 1 2 2 2 1 2 3 3 3 3 1 2 3
## [106] 2 2 3 2 3 1 3 3 2 4 2 2 3 2 3 2 2 3 2 4 1 4 3 3 2 2 2 3 3 3 2 3 3 3 4
## [141] 3 1 3 3 2 1 3 3 3 3 3 3 3 1 2 2 2 3 3 1 1 1 4 2 4 2 4 3 2 3 3 3 3 1 4
## [176] 3 4 1 1 3 3 4 2 3 2 3 4 2 4 2 3 2 3 3 3 3 2 3 2 3 3 4 4 3 1 3 3 4 3 2
## [211] 3 3 2 3 4 3 3 3 1 2 3 4 1 3 3 3 3 2 3 3 1 4 1 3 3 1 1 1 1 3 2 2 3 3 1
## [246] 3 3 3 3 3 3 2 3 2 3 3 3 1 4 3 2 3 2 3 2 3 3 3 3 2 4 4 3 3 3 3 2 2 3 3
## [281] 4 3 3 4 2 3 3 3 2 2 2 3 3 3 3 3 1 2 2 2 3 3 3 2 3 3 2 3 3 2 2 2 2 2 4
## [316] 2 3 3 2 2 1 2 4 2 3 3 2 3 3 3 2 3 3 3 3 3 2 2 3 3 3 3 4 3 3 3 2 2 3 3
## [351] 3 3 2 3 2 3 2 2 2 2 3 3 2 2 2 2 3 2 3 3 3 3 3 3 1 3 3 3 2 3 3 3 2 3 2
## [386] 3 3 2 3 3 2 2 3 3 2 3 2 3 3 3 3 2 3 2 3 3 3 3 2 2 3 3 2 3 3 3 2 3 3 3
## [421] 4 3 3 3 3 2 3 3 2 3 4 3 3 3 3 3 2 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [456] 3 3 2 2 2 2 3 3 2 2 1 3 3 3 3 3 3 3 3 2 4 2 2 3 2 3 3 2 2 3 3 3 3 2 3
## [491] 3 2 3 3 2 2 3 2 2 3 3 2 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 2 3 3 3 3 3 4 3
## [526] 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 2 3 3 3 2 3 3 3 3 2 2
## [561] 3 3 2 2 2 3 3 3 3 2 3 2 3 2 4 3 2 3 3 3 3 3 2 3 3 2 2 3 3 2 3 3 3 3 3
## [596] 3 3 4 3 3
##
## Within cluster sum of squares by cluster:
## [1] 1180.009 6396.420 5146.643 3181.195
## (between_SS / total_SS = 24.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
fviz_cluster(k4, data = BS)
k4center <- as.data.frame(k4$centers)
colnames(k4center) <- c("No.Br","Br.Runs","Total.Vol","No.Trans","Value","Trans.Br.Runs","Vol.Tran","Avg.Price","PVNP","PVP6","PVOP","Br.57.144","Br.55","Br.272","Br.286","Br.24","Br.481","Br.352","Br.5","Br.999","Pt1","Pt2","Pt3","Pt4","Pt5","Pt6","Pt7","Pt8","Pt9","Pt10","Pt11","Pt12","Pt13","Pt14","Pt15")
cluster <- matrix(c("1","2","3","4"),nrow = 4)
k4center <- cbind(cluster,k4center)
Purchase summary over the period, k = 4
ggparcoord(k4center, columns = 2:9, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Brandwise purchase, k = 4
ggparcoord(k4center, columns = 13:21, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Price categorywise purchase, k = 4
ggparcoord(k4center, columns = 22:25, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
Selling propositionwise purchase, k = 4
ggparcoord(k4center, columns = 26:36, groupColumn = 1, showPoints = TRUE, alphaLines = 0.3)
By comparing k2, k3 and k4, I think k3 is the best segmentation. Because the clusters from k2 is too broad to achieve the purpose of market segmentation, and two clusters cannot properly characterize the data. And the clusters from k4 is too narrow for market segmentation, some clusters have similar characteristics.
Features of cluster 1: Low customer loyalty, low sensitivity to discount, relatively average purchase rate of different brands, no specific requirements on the price and type of goods, low number of purchased brands, low continuous purchase volume, low total purchase volume, low average single purchase volume, low total purchase value but high average purchase price
Features of cluster 2: High customer loyalty, low number of brands purchased, high purchase volume for specific brands, high purchase volume for certain types of products and certain price level, different sensitivity to different discount, low number of consecutive purchases, high average purchase volume but low total value
Features of cluster 3: Medium customer loyalty, high sensitivity to discount (increase purchases for specific discount), strong purchase desire, medium purchase volume of each brand, high number of brands purchased, high total purchase volume and total value, no specific requirements on the price and type of goods, tend to consecutive purchase
Build the model
BS1 <- scale(BathSoap)
BS2 <- BathSoap[,-c(1:11)]
set.seed(120)
Model_k3 <- kcca(BS2, k = 3, kccaFamily("kmeans"))
Model_k3
## kcca object of family 'kmeans'
##
## call:
## kcca(x = BS2, k = 3, family = kccaFamily("kmeans"))
##
## cluster sizes:
##
## 1 2 3
## 35 351 214
k3_cluster <- predict(Model_k3, BS2)
cluster_data <- data.frame(cluster = k3_cluster)
cluster_data <- cbind(cluster_data, BS1)
cluster_data$cluster <- ifelse(cluster_data$cluster==1,1,0)
head(cluster_data)
## cluster Member.id SEC FEH MT SEX
## 1 0 -2.065230 1.3405223 0.83849915 0.4241721 -1.1385078
## 2 0 -2.065011 0.4468408 -0.04258577 0.4241721 0.4034892
## 3 0 -1.977294 -0.4468408 0.83849915 0.4241721 0.4034892
## 4 0 -1.977075 1.3405223 -1.80475562 -1.9043115 -2.6805048
## 5 0 -1.973566 1.3405223 -0.92367070 0.4241721 0.4034892
## 6 0 -1.911507 1.3405223 0.83849915 0.4241721 0.4034892
## AGE EDU HS CHILD CS Affluence.Index
## 1 0.9089277 -0.01978688 -0.95286121 0.6299072 0.134681 -1.3163799
## 2 -1.4019055 -0.01978688 -0.08333007 -1.0133290 0.134681 0.1735308
## 3 0.9089277 0.43683338 0.78620108 0.6299072 0.134681 0.5240980
## 4 0.9089277 -1.84626790 -1.82239236 1.4515254 -1.836260 -1.4916636
## 5 -0.2464889 -0.01978688 -0.08333007 -0.1917109 0.134681 -0.6152455
## 6 -0.2464889 -0.01978688 0.35143550 -1.0133290 0.134681 -0.3523201
## No..of.Brands Brand.Runs Total.Volume No..of..Trans Value
## 1 -0.4030277 0.1200727 -0.5005898 -0.4104681 -0.5881031
## 2 0.8630280 0.8895639 0.2651391 0.5076339 0.3896410
## 3 0.8630280 2.0438006 1.4394712 1.8274054 0.6936645
## 4 -1.0360556 -1.1303505 -1.3403176 -1.5580955 -1.3852447
## 5 -0.4030277 -0.9379777 -0.4651989 -1.0416632 -0.8451360
## 6 -0.4030277 0.9857502 0.8056536 0.5650152 0.4168163
## Trans...Brand.Runs Vol.Tran Avg..Price Pur.Vol.No.Promo....
## 1 -0.4636969 -0.3242918 -0.43944366 -0.3943558
## 2 -0.3907514 -0.2639930 0.05217678 0.1983882
## 3 -0.3523590 -0.1944886 -0.90701745 1.4444233
## 4 -0.6211057 -0.1610026 -1.13145287 -1.2708284
## 5 -0.1719147 0.8980852 -1.25970168 -0.7922274
## 6 -0.3984298 0.1135176 -0.65586353 0.9690461
## Pur.Vol.Promo.6.. Pur.Vol.Other.Promo.. Br..Cd..57..144 Br..Cd..55
## 1 -0.5574329 -0.5116939 0.2226321 -0.1754393
## 2 0.7686897 -0.1252184 -0.4805832 -0.1578175
## 3 -0.1190296 0.7659568 -0.3756086 2.5737501
## 4 -0.5574329 -0.5116939 -0.3992184 -0.2092097
## 5 0.5452179 2.2427218 -0.4461840 -0.1474447
## 6 -0.5574329 -0.5116939 -0.1824148 -0.1214539
## Br..Cd..272 Br..Cd..286 Br..Cd..24 Br..Cd..481 Br..Cd..352 Br..Cd..5
## 1 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## 2 -0.3448178 -0.2253619 -0.2371511 0.4047639 -0.2549821 2.6176765
## 3 -0.3448178 0.1428499 -0.2371511 -0.2538919 -0.2549821 0.3943628
## 4 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## 5 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## 6 -0.3448178 -0.2253619 -0.2371511 -0.2538919 -0.2549821 -0.2929379
## Others.999 Pr.Cat.1 Pr.Cat.2 Pr.Cat.3 Pr.Cat.4 PropCat.5
## 1 -0.3870628 -0.2794416143 -0.2520866 -0.1988988 -0.1925159 -0.246319991
## 2 0.6565013 0.3854838369 0.2625352 -0.1496659 -0.1056810 0.141979507
## 3 0.4747587 -0.0003808082 0.2150992 2.5307700 -0.3687742 -0.000177325
## 4 -1.0949914 -0.8355295851 -0.8798374 -0.2317780 -0.3687742 -0.794776958
## 5 0.1059753 -0.8355295851 -0.9096612 -0.1716428 1.7406778 0.189311544
## 6 1.6977747 0.3691393848 0.3419310 -0.1463378 1.1709563 0.540123103
## PropCat.6 PropCat.7 PropCat.8 PropCat.9 PropCat.10 PropCat.11
## 1 -0.4980207 -0.4173594 -0.5069920 -0.4317488 -0.2850019 -0.2651424
## 2 1.6247065 -0.2567103 -0.3267324 -0.2787491 -0.2850019 0.3729320
## 3 0.7049846 -0.1518142 -0.3580118 -0.1788477 -0.2850019 -0.2651424
## 4 -0.4980207 -0.4173594 -0.5069920 -0.4317488 -0.2850019 -0.2651424
## 5 -0.4980207 -0.4173594 -0.2393435 -0.4317488 -0.2850019 -0.2651424
## 6 0.2907463 -0.4173594 -0.3897749 0.9611232 -0.2850019 -0.2651424
## PropCat.12 PropCat.13 PropCat.14 PropCat.15
## 1 0.8905060 -0.2536688 -0.1912455 2.1061267
## 2 -0.2907978 -0.2536688 -0.1739337 -0.2505867
## 3 1.9761280 -0.2536688 2.5630697 -0.2505867
## 4 -0.2907978 -0.2536688 -0.2244217 -0.2505867
## 5 -0.2907978 -0.2536688 -0.1637435 -0.2505867
## 6 -0.2907978 -0.2536688 -0.1382100 3.9879993
cluster_data$cluster <- as.factor(cluster_data$cluster)
str(cluster_data)
## 'data.frame': 600 obs. of 47 variables:
## $ cluster : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## $ Member.id : num -2.07 -2.07 -1.98 -1.98 -1.97 ...
## $ SEC : num 1.341 0.447 -0.447 1.341 1.341 ...
## $ FEH : num 0.8385 -0.0426 0.8385 -1.8048 -0.9237 ...
## $ MT : num 0.424 0.424 0.424 -1.904 0.424 ...
## $ SEX : num -1.139 0.403 0.403 -2.681 0.403 ...
## $ AGE : num 0.909 -1.402 0.909 0.909 -0.246 ...
## $ EDU : num -0.0198 -0.0198 0.4368 -1.8463 -0.0198 ...
## $ HS : num -0.9529 -0.0833 0.7862 -1.8224 -0.0833 ...
## $ CHILD : num 0.63 -1.013 0.63 1.452 -0.192 ...
## $ CS : num 0.135 0.135 0.135 -1.836 0.135 ...
## $ Affluence.Index : num -1.316 0.174 0.524 -1.492 -0.615 ...
## $ No..of.Brands : num -0.403 0.863 0.863 -1.036 -0.403 ...
## $ Brand.Runs : num 0.12 0.89 2.044 -1.13 -0.938 ...
## $ Total.Volume : num -0.501 0.265 1.439 -1.34 -0.465 ...
## $ No..of..Trans : num -0.41 0.508 1.827 -1.558 -1.042 ...
## $ Value : num -0.588 0.39 0.694 -1.385 -0.845 ...
## $ Trans...Brand.Runs : num -0.464 -0.391 -0.352 -0.621 -0.172 ...
## $ Vol.Tran : num -0.324 -0.264 -0.194 -0.161 0.898 ...
## $ Avg..Price : num -0.4394 0.0522 -0.907 -1.1315 -1.2597 ...
## $ Pur.Vol.No.Promo.... : num -0.394 0.198 1.444 -1.271 -0.792 ...
## $ Pur.Vol.Promo.6.. : num -0.557 0.769 -0.119 -0.557 0.545 ...
## $ Pur.Vol.Other.Promo..: num -0.512 -0.125 0.766 -0.512 2.243 ...
## $ Br..Cd..57..144 : num 0.223 -0.481 -0.376 -0.399 -0.446 ...
## $ Br..Cd..55 : num -0.175 -0.158 2.574 -0.209 -0.147 ...
## $ Br..Cd..272 : num -0.345 -0.345 -0.345 -0.345 -0.345 ...
## $ Br..Cd..286 : num -0.225 -0.225 0.143 -0.225 -0.225 ...
## $ Br..Cd..24 : num -0.237 -0.237 -0.237 -0.237 -0.237 ...
## $ Br..Cd..481 : num -0.254 0.405 -0.254 -0.254 -0.254 ...
## $ Br..Cd..352 : num -0.255 -0.255 -0.255 -0.255 -0.255 ...
## $ Br..Cd..5 : num -0.293 2.618 0.394 -0.293 -0.293 ...
## $ Others.999 : num -0.387 0.657 0.475 -1.095 0.106 ...
## $ Pr.Cat.1 : num -0.279442 0.385484 -0.000381 -0.83553 -0.83553 ...
## $ Pr.Cat.2 : num -0.252 0.263 0.215 -0.88 -0.91 ...
## $ Pr.Cat.3 : num -0.199 -0.15 2.531 -0.232 -0.172 ...
## $ Pr.Cat.4 : num -0.193 -0.106 -0.369 -0.369 1.741 ...
## $ PropCat.5 : num -0.24632 0.14198 -0.000177 -0.794777 0.189312 ...
## $ PropCat.6 : num -0.498 1.625 0.705 -0.498 -0.498 ...
## $ PropCat.7 : num -0.417 -0.257 -0.152 -0.417 -0.417 ...
## $ PropCat.8 : num -0.507 -0.327 -0.358 -0.507 -0.239 ...
## $ PropCat.9 : num -0.432 -0.279 -0.179 -0.432 -0.432 ...
## $ PropCat.10 : num -0.285 -0.285 -0.285 -0.285 -0.285 ...
## $ PropCat.11 : num -0.265 0.373 -0.265 -0.265 -0.265 ...
## $ PropCat.12 : num 0.891 -0.291 1.976 -0.291 -0.291 ...
## $ PropCat.13 : num -0.254 -0.254 -0.254 -0.254 -0.254 ...
## $ PropCat.14 : num -0.191 -0.174 2.563 -0.224 -0.164 ...
## $ PropCat.15 : num 2.106 -0.251 -0.251 -0.251 -0.251 ...
Q3_Model <- glm(cluster~.,family="binomial", data=cluster_data)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(Q3_Model)
##
## Call:
## glm(formula = cluster ~ ., family = "binomial", data = cluster_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.483e-05 -2.100e-08 -2.100e-08 -2.100e-08 4.701e-05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.413e+01 1.842e+04 -0.004 0.997
## Member.id -2.957e+00 2.916e+04 0.000 1.000
## SEC 4.084e-01 3.253e+04 0.000 1.000
## FEH 2.536e+00 3.639e+04 0.000 1.000
## MT 5.160e+00 3.916e+04 0.000 1.000
## SEX -5.521e+00 7.445e+04 0.000 1.000
## AGE 3.574e+00 3.860e+04 0.000 1.000
## EDU 6.133e+00 4.465e+04 0.000 1.000
## HS 1.697e-01 2.688e+04 0.000 1.000
## CHILD 5.833e+00 2.644e+04 0.000 1.000
## CS 1.096e-01 3.524e+04 0.000 1.000
## Affluence.Index -3.505e-01 4.788e+04 0.000 1.000
## No..of.Brands -6.710e+00 3.191e+04 0.000 1.000
## Brand.Runs 2.489e+00 4.892e+04 0.000 1.000
## Total.Volume -7.538e+02 3.655e+06 0.000 1.000
## No..of..Trans 9.948e+00 5.935e+04 0.000 1.000
## Value 4.056e+00 2.232e+05 0.000 1.000
## Trans...Brand.Runs -1.234e+01 5.073e+04 0.000 1.000
## Vol.Tran 1.304e+01 5.939e+04 0.000 1.000
## Avg..Price -1.290e+00 6.940e+04 0.000 1.000
## Pur.Vol.No.Promo.... 1.794e+02 3.605e+06 0.000 1.000
## Pur.Vol.Promo.6.. 2.121e+01 5.054e+05 0.000 1.000
## Pur.Vol.Other.Promo.. 1.656e+01 3.369e+05 0.000 1.000
## Br..Cd..57..144 -2.163e+00 1.093e+06 0.000 1.000
## Br..Cd..55 -2.623e+01 1.142e+06 0.000 1.000
## Br..Cd..272 -3.952e+00 2.742e+05 0.000 1.000
## Br..Cd..286 -9.488e-01 5.200e+05 0.000 1.000
## Br..Cd..24 -3.764e+00 1.890e+05 0.000 1.000
## Br..Cd..481 -1.251e+01 3.481e+05 0.000 1.000
## Br..Cd..352 3.833e-01 4.486e+05 0.000 1.000
## Br..Cd..5 1.901e+00 1.860e+05 0.000 1.000
## Others.999 -2.287e+00 1.542e+06 0.000 1.000
## Pr.Cat.1 1.169e+02 9.200e+05 0.000 1.000
## Pr.Cat.2 2.414e+02 1.738e+06 0.000 1.000
## Pr.Cat.3 1.866e+02 1.318e+06 0.000 1.000
## Pr.Cat.4 1.156e+02 8.893e+05 0.000 1.000
## PropCat.5 2.484e+02 9.033e+05 0.000 1.000
## PropCat.6 8.864e+01 3.313e+05 0.000 1.000
## PropCat.7 1.015e+02 3.772e+05 0.000 1.000
## PropCat.8 6.041e+01 2.350e+05 0.000 1.000
## PropCat.9 3.357e+01 1.394e+05 0.000 1.000
## PropCat.10 3.255e+01 7.713e+04 0.000 1.000
## PropCat.11 6.205e+01 2.107e+05 0.000 1.000
## PropCat.12 9.660e+00 3.687e+04 0.000 1.000
## PropCat.13 4.071e+01 1.826e+05 0.000 1.000
## PropCat.14 1.605e+02 6.381e+05 0.000 1.000
## PropCat.15 4.391e+01 1.693e+05 0.000 1.000
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2.6683e+02 on 599 degrees of freedom
## Residual deviance: 2.9285e-08 on 553 degrees of freedom
## AIC: 94
##
## Number of Fisher Scoring iterations: 25
